Designing fuel cell catalyst support for superior catalytic activity and low mass-transport resistance
Bibliographic record
Abstract
Abstract The development of low-Platinum content polymer electrolyte fuel cells (PEFCs) has been hindered by inexplicable reduction of oxygen reduction reaction (ORR) activity and unexpected O2 mass transport resistance when catalysts have been interfaced with ionomer in a cathode catalyst layer. In this study, we introduce a bottom-up designed spherical carbon support with intrinsic Nitrogen-doping that permits uniform dispersion of Pt catalyst, which reproducibly exhibits high ORR mass activity of 638 ± 68 mA mgPt−1 at 0.9 V and 100% relative humidity (RH) in a membrane electrode assembly. The uniformly distributed Nitrogen-functional surface groups on the carbon support surface promote high ionomer coverage directly evidenced by high-resolution electron microscopy and nearly humidity-independent double layer capacitance. The hydrophilic nature of the carbon surface appears to ensure high activity and performance for operation over a broad range of RH. The paradigm challenging large carbon support (~135 nm) combined with favourable ionomer film structure, hypothesized recently to arise from the interactions of an ionic moiety of the ionomer and Nitrogen-functional group of the catalyst support, results in an unprecedented low local oxygen transport resistance (5.0 s cm−1) for ultra-low Pt loading (34 ± 2 μgPt cm−2) catalyst layer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".